Physics-Informed Neural Networks for Device and Circuit Modeling: A Case Study of NeuroSPICE

This paper presents the application of Physics-Informed Neural Networks (PINNs) for modeling semiconductor devices and electronic circuits, using NeuroSPICE as a case study. This approach integrates physical laws directly into neural network training, leading to more accurate and physically consistent models, which is crucial for advanced engineering design and optimization.